Antenna Season Report Notebook¶

Josh Dillon, Last Revised January 2022

This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.

In [1]:
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
In [2]:
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
In [3]:
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "95"
csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
In [4]:
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))

Antenna 95 Report

In [5]:
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
In [6]:
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 92 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_
Found 90 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
In [7]:
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'

Load relevant info from summary CSVs¶

In [8]:
this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
            antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
        else:
            antnum = int(row['Ant'])
        if antnum != int(antenna):
            continue
        
        if np.issubdtype(type(row['Node']), np.integer):
            row['Node'] = str(row['Node'])
        if type(row['Node']) == str and row['Node'].isnumeric():
            row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break
In [9]:
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])

Table 1: Per-Night RTP Summary Info For This Atenna¶

This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.

In [10]:
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))

Antenna 95, Node N11:

Out[10]:
JDs A Priori Status Auto Metrics Flags Dead Fraction in Ant Metrics (Jee) Dead Fraction in Ant Metrics (Jnn) Crossed Fraction in Ant Metrics Flag Fraction Before Redcal Flagged By Redcal chi^2 Fraction ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score Average Dead Ant Metric (Jee) Average Dead Ant Metric (Jnn) Average Crossed Ant Metric Median chi^2 Per Antenna (Jee) Median chi^2 Per Antenna (Jnn)
2459909 not_connected 0.00% 0.00% 0.00% 0.00% - - 0.476974 -0.078560 -0.912768 0.360036 -0.117043 -1.787716 0.696453 0.156398 0.6288 0.6685 0.4140 nan nan
2459908 not_connected 0.00% 0.00% 0.00% 0.00% - - 0.548376 -0.169075 -0.724335 0.361355 -0.480033 -1.607993 0.237840 0.047142 0.6332 0.6747 0.4126 nan nan
2459907 not_connected 100.00% 0.00% 0.00% 0.00% - - 0.789352 -0.226925 -0.758774 0.363205 -0.186883 -1.639364 1.069284 5.016798 0.6315 0.6751 0.4113 nan nan
2459906 not_connected 100.00% 0.00% 0.00% 0.00% - - 0.831997 -0.134143 -0.403640 1.002285 -0.435475 -0.438364 0.652318 7.239838 0.6217 0.6685 0.4140 nan nan
2459905 not_connected 100.00% 0.00% 0.00% 0.00% - - 0.861688 0.022777 -0.655696 0.902529 -0.368668 -1.188652 1.474945 11.952297 0.6153 0.6626 0.4141 nan nan
2459904 not_connected 100.00% 0.00% 0.00% 0.00% - - 0.470194 -0.029745 -0.403826 1.203381 -0.275988 -0.509059 -1.122361 12.285899 0.6210 0.6640 0.4083 nan nan
2459903 not_connected 100.00% 0.00% 0.00% 0.00% - - 0.230467 0.072940 -0.537799 0.890963 -0.156108 -0.523421 -0.712783 5.846118 0.6279 0.6683 0.4145 nan nan
2459902 not_connected 100.00% 0.00% 0.00% 0.00% - - 0.873051 0.503541 -0.450338 1.294937 -0.195083 -0.461751 2.153794 5.067071 0.6312 0.6676 0.4047 nan nan
2459901 not_connected 0.00% 0.00% 0.00% 0.00% - - 0.286684 -0.149090 -0.843834 0.568624 0.326642 -0.824593 0.097072 2.104209 0.6243 0.6646 0.4138 nan nan
2459900 not_connected 0.00% 0.00% 0.00% 0.00% - - 0.166254 -0.082981 -0.886851 0.599793 0.531889 -0.896622 0.254596 -0.152249 0.5732 0.6302 0.3516 nan nan
2459898 not_connected 0.00% 0.00% 0.00% 0.00% - - 0.081152 -0.271471 -0.732603 0.672905 -0.288075 -1.038623 -1.209632 -1.143846 0.6319 0.6711 0.4104 nan nan
2459897 not_connected 0.00% 0.00% 0.00% 0.00% - - 0.397888 -0.040839 -0.395085 0.838963 -0.082943 -0.889262 0.665138 2.311520 0.6371 0.6751 0.4035 nan nan
2459896 not_connected 0.00% 0.00% 0.00% 0.00% - - 0.164480 0.051773 -0.433838 0.823834 -0.611024 -1.290687 2.575501 3.415780 0.6423 0.6786 0.4035 nan nan
2459895 not_connected 0.00% 0.00% 0.00% 0.00% - - 0.691551 -0.180411 -0.732994 1.734579 -1.670251 -0.132043 -1.364474 0.388515 0.7368 0.7539 0.2861 nan nan
2459894 not_connected 0.00% 0.00% 0.00% 0.00% - - 0.437684 -0.382683 -0.576665 0.791063 -0.675394 -1.186231 0.064845 1.743457 0.6466 0.6794 0.3914 nan nan
2459893 not_connected 0.00% 0.00% 0.00% 0.00% - - 0.026259 -0.383761 -0.674021 0.574741 -0.717971 -1.205448 -0.000721 1.441657 0.6484 0.6803 0.3943 nan nan
2459892 not_connected 0.00% 0.00% 0.00% 0.00% - - 0.329606 -0.239003 -0.676790 0.842370 0.131635 -1.007107 1.010335 2.854343 0.6410 0.6791 0.3997 nan nan
2459891 not_connected 0.00% 0.00% 0.00% 0.00% - - 0.051602 -0.393956 -0.437580 1.044443 -0.289417 -1.616502 -1.137607 -0.741813 0.6345 0.6726 0.4058 nan nan
2459890 not_connected 0.00% 0.00% 0.00% 0.00% - - 1.020407 -0.354809 -0.919529 1.616054 -0.397970 -0.889917 1.842396 1.461810 0.6339 0.6692 0.4006 nan nan
2459889 not_connected 0.00% 0.00% 0.00% 0.00% - - 0.612306 -0.338221 -0.535396 1.003182 -0.286008 -1.638721 0.126476 1.215980 0.6436 0.6740 0.3961 nan nan
2459888 not_connected 0.00% 0.00% 0.00% 0.00% - - 0.514188 -0.200070 -0.636541 1.406185 -0.609412 -0.993770 1.128034 1.354739 0.6613 0.6925 0.3899 nan nan
2459887 not_connected 0.00% 0.00% 0.00% 0.00% - - 0.221258 -0.554298 -1.639626 0.624966 -1.503525 -2.001672 0.797743 0.379853 0.6471 0.6755 0.4018 nan nan
2459886 not_connected 100.00% 0.00% 0.00% 0.00% - - 0.279549 -0.851047 -0.576653 1.025857 5.060935 2.180329 -0.740210 0.199277 0.7213 0.7265 0.3514 nan nan
2459885 not_connected 100.00% 0.00% 0.00% 0.00% - - 2.695861 1.557713 16.171541 29.447813 3.278362 4.495643 5.386560 6.053138 0.6946 0.7090 0.3638 nan nan
2459884 not_connected 100.00% 0.00% 0.00% 0.00% - - 2.280674 3.937696 3.634070 4.624247 2.633001 5.211318 -1.735213 -2.526394 0.6556 0.6548 0.3997 nan nan
2459883 not_connected 100.00% 0.00% 0.00% 100.00% - - 6.611531 5.955657 39.892401 39.854328 4.886329 8.962299 -5.037797 -3.605181 0.2728 0.2759 -0.2967 nan nan
2459882 not_connected 100.00% 0.00% 0.00% 97.95% - - 10.148774 9.274650 47.467838 46.182966 6.600504 12.001650 -1.943426 -1.835854 0.2841 0.2849 -0.2939 nan nan
2459881 not_connected 100.00% 0.00% 0.00% 82.20% - - 6.820248 5.995837 53.276689 52.203485 12.289803 22.737913 -3.953096 5.347069 0.3898 0.3888 -0.2496 nan nan
2459880 not_connected 100.00% 0.00% 0.00% 99.66% - - 7.299539 6.642049 42.541963 42.480060 4.094876 7.694820 -2.440581 -2.200268 0.2646 0.2661 -0.3068 nan nan
2459879 not_connected 0.00% 0.00% 0.00% 100.00% - - 2.730598 2.260675 1.902224 1.570610 -0.261961 0.706599 -3.656034 -3.695475 0.2281 0.2242 -0.2927 nan nan
2459878 not_connected 100.00% 0.00% 0.00% 100.00% - - 6.621751 6.807458 51.766945 51.922227 7.178234 12.856198 -4.688550 -5.024469 0.2446 0.2431 -0.2822 nan nan

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

In [11]:
htmls_to_display = []
for am_html in auto_metric_htmls:
    html_to_display = ''
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line:
            htmls_to_display.append(html_to_display)
            break

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.

In [12]:
for i, html_to_display in enumerate(htmls_to_display):
    if i == 100:
        break
    display(HTML(html_to_display))

Antenna 95: 2459909

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
95 N11 not_connected ee Temporal Discontinuties 0.696453 -0.078560 0.476974 0.360036 -0.912768 -1.787716 -0.117043 0.156398 0.696453

Antenna 95: 2459908

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
95 N11 not_connected ee Shape 0.548376 0.548376 -0.169075 -0.724335 0.361355 -0.480033 -1.607993 0.237840 0.047142

Antenna 95: 2459907

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
95 N11 not_connected nn Temporal Discontinuties 5.016798 -0.226925 0.789352 0.363205 -0.758774 -1.639364 -0.186883 5.016798 1.069284

Antenna 95: 2459906

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
95 N11 not_connected nn Temporal Discontinuties 7.239838 -0.134143 0.831997 1.002285 -0.403640 -0.438364 -0.435475 7.239838 0.652318

Antenna 95: 2459905

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
95 N11 not_connected nn Temporal Discontinuties 11.952297 0.022777 0.861688 0.902529 -0.655696 -1.188652 -0.368668 11.952297 1.474945

Antenna 95: 2459904

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
95 N11 not_connected nn Temporal Discontinuties 12.285899 -0.029745 0.470194 1.203381 -0.403826 -0.509059 -0.275988 12.285899 -1.122361

Antenna 95: 2459903

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
95 N11 not_connected nn Temporal Discontinuties 5.846118 0.072940 0.230467 0.890963 -0.537799 -0.523421 -0.156108 5.846118 -0.712783

Antenna 95: 2459902

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
95 N11 not_connected nn Temporal Discontinuties 5.067071 0.873051 0.503541 -0.450338 1.294937 -0.195083 -0.461751 2.153794 5.067071

Antenna 95: 2459901

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
95 N11 not_connected nn Temporal Discontinuties 2.104209 0.286684 -0.149090 -0.843834 0.568624 0.326642 -0.824593 0.097072 2.104209

Antenna 95: 2459900

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
95 N11 not_connected nn Power 0.599793 0.166254 -0.082981 -0.886851 0.599793 0.531889 -0.896622 0.254596 -0.152249

Antenna 95: 2459898

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
95 N11 not_connected nn Power 0.672905 -0.271471 0.081152 0.672905 -0.732603 -1.038623 -0.288075 -1.143846 -1.209632

Antenna 95: 2459897

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
95 N11 not_connected nn Temporal Discontinuties 2.311520 -0.040839 0.397888 0.838963 -0.395085 -0.889262 -0.082943 2.311520 0.665138

Antenna 95: 2459896

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
95 N11 not_connected nn Temporal Discontinuties 3.415780 0.051773 0.164480 0.823834 -0.433838 -1.290687 -0.611024 3.415780 2.575501

Antenna 95: 2459895

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
95 N11 not_connected nn Power 1.734579 0.691551 -0.180411 -0.732994 1.734579 -1.670251 -0.132043 -1.364474 0.388515

Antenna 95: 2459894

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
95 N11 not_connected nn Temporal Discontinuties 1.743457 -0.382683 0.437684 0.791063 -0.576665 -1.186231 -0.675394 1.743457 0.064845

Antenna 95: 2459893

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
95 N11 not_connected nn Temporal Discontinuties 1.441657 0.026259 -0.383761 -0.674021 0.574741 -0.717971 -1.205448 -0.000721 1.441657

Antenna 95: 2459892

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
95 N11 not_connected nn Temporal Discontinuties 2.854343 -0.239003 0.329606 0.842370 -0.676790 -1.007107 0.131635 2.854343 1.010335

Antenna 95: 2459891

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
95 N11 not_connected nn Power 1.044443 0.051602 -0.393956 -0.437580 1.044443 -0.289417 -1.616502 -1.137607 -0.741813

Antenna 95: 2459890

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
95 N11 not_connected ee Temporal Discontinuties 1.842396 -0.354809 1.020407 1.616054 -0.919529 -0.889917 -0.397970 1.461810 1.842396

Antenna 95: 2459889

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
95 N11 not_connected nn Temporal Discontinuties 1.215980 0.612306 -0.338221 -0.535396 1.003182 -0.286008 -1.638721 0.126476 1.215980

Antenna 95: 2459888

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
95 N11 not_connected nn Power 1.406185 -0.200070 0.514188 1.406185 -0.636541 -0.993770 -0.609412 1.354739 1.128034

Antenna 95: 2459887

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
95 N11 not_connected ee Temporal Discontinuties 0.797743 -0.554298 0.221258 0.624966 -1.639626 -2.001672 -1.503525 0.379853 0.797743

Antenna 95: 2459886

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
95 N11 not_connected ee Temporal Variability 5.060935 0.279549 -0.851047 -0.576653 1.025857 5.060935 2.180329 -0.740210 0.199277

Antenna 95: 2459885

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
95 N11 not_connected nn Power 29.447813 1.557713 2.695861 29.447813 16.171541 4.495643 3.278362 6.053138 5.386560

Antenna 95: 2459884

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
95 N11 not_connected nn Temporal Variability 5.211318 3.937696 2.280674 4.624247 3.634070 5.211318 2.633001 -2.526394 -1.735213

Antenna 95: 2459883

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
95 N11 not_connected ee Power 39.892401 5.955657 6.611531 39.854328 39.892401 8.962299 4.886329 -3.605181 -5.037797

Antenna 95: 2459882

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
95 N11 not_connected ee Power 47.467838 9.274650 10.148774 46.182966 47.467838 12.001650 6.600504 -1.835854 -1.943426

Antenna 95: 2459881

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
95 N11 not_connected ee Power 53.276689 5.995837 6.820248 52.203485 53.276689 22.737913 12.289803 5.347069 -3.953096

Antenna 95: 2459880

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
95 N11 not_connected ee Power 42.541963 6.642049 7.299539 42.480060 42.541963 7.694820 4.094876 -2.200268 -2.440581

Antenna 95: 2459879

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
95 N11 not_connected ee Shape 2.730598 2.260675 2.730598 1.570610 1.902224 0.706599 -0.261961 -3.695475 -3.656034

Antenna 95: 2459878

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
95 N11 not_connected nn Power 51.922227 6.807458 6.621751 51.922227 51.766945 12.856198 7.178234 -5.024469 -4.688550
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